From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data

Author:

Liu Ye1,Zhu Lei2,Pei Shunda1,Fu Huazhu3,Qin Jing4,Zhang Qing5,Wan Liang1,Feng Wei1

Affiliation:

1. Tianjin University, Tianjin, China

2. University of Cambridge, Cambridge, United Kingdom

3. Inception Institute of Artificial Intelligence, Abu Dhabi, UAE

4. Hong Kong Polytechnic University, Hong Kong , China

5. Sun Yat-sen University, Guangzhou, China

Funder

the National Natural Science Foundation of China

Innovation and Technology Fund - Midstream Research Programme for Universities (ITF-MRP)

the research fund for The Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China

Publisher

ACM

Cited by 52 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Self-guided disentangled representation learning for single image dehazing;Neural Networks;2024-04

2. Image Restoration via Frequency Selection;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-02

3. DPAFD-net: A dual-path adaptive fusion dehazing network;Journal of Visual Communication and Image Representation;2024-02

4. Degradation-adaptive neural network for jointly single image dehazing and desnowing;Frontiers of Computer Science;2024-01-13

5. DEA-Net: Single Image Dehazing Based on Detail-Enhanced Convolution and Content-Guided Attention;IEEE Transactions on Image Processing;2024

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